This notebook illustrates using clustering and our descriptors compounds.

In [3]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.offline as pyo
import plotly.graph_objs as go
pyo.init_notebook_mode()


from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.metrics import silhouette_score
from yellowbrick.cluster import KElbowVisualizer



import warnings
warnings.filterwarnings("ignore")
In [4]:
# import data
data = pd.read_csv("../../../data/processed/drug_bank_clean.csv")
In [5]:
data.head(2)
Out[5]:
Name SMILES ALogP ALogp2 AMR apol nAcid naAromAtom nAromBond nAtom ... MW WTPT-1 WTPT-2 WTPT-3 WTPT-4 WTPT-5 WPATH WPOL XLogP Zagreb
0 Bivalirudin CC[C@H](C)[C@H](NC(=O)[C@H](CCC(O)=O)NC(=O)[C@... -5.0931 25.939668 534.3813 317.363434 6 18 18 293 ... 2180.289181 306.516036 1.977523 153.54308 82.176355 71.366725 286334.0 230.0 -8.623 752.0
1 Leuprolide CCNC(=O)[C@@H]1CCCN1C(=O)[C@H](CCCNC(N)=N)NC(=... 0.4325 0.187056 317.0896 187.074612 0 20 21 171 ... 1209.400552 174.200943 2.002310 78.03037 30.548034 47.482336 46759.0 130.0 1.134 436.0

2 rows × 224 columns

In [6]:
# getting all descriptors except compound name
without_compound = data.drop(['Name','SMILES'], axis=1)
without_compound.head(2)
Out[6]:
ALogP ALogp2 AMR apol nAcid naAromAtom nAromBond nAtom ATSc1 ATSc2 ... MW WTPT-1 WTPT-2 WTPT-3 WTPT-4 WTPT-5 WPATH WPOL XLogP Zagreb
0 -5.0931 25.939668 534.3813 317.363434 6 18 18 293 5.125258 -2.388694 ... 2180.289181 306.516036 1.977523 153.54308 82.176355 71.366725 286334.0 230.0 -8.623 752.0
1 0.4325 0.187056 317.0896 187.074612 0 20 21 171 2.269431 -1.093604 ... 1209.400552 174.200943 2.002310 78.03037 30.548034 47.482336 46759.0 130.0 1.134 436.0

2 rows × 222 columns

In [7]:
# scaling our data by using standard scaler
scaler = StandardScaler()
X_scaled = scaler.fit_transform(without_compound)
In [8]:
# Instantiate the KMeans model
kmeans_model = KMeans()

# Instantiate the KElbowVisualizer
elbow_visualizer = KElbowVisualizer(kmeans_model, k=(2, 10))

# Fit the visualizer to the data
elbow_visualizer.fit(X_scaled)

# Visualize the elbow plot
elbow_visualizer.show()
Out[8]:
<AxesSubplot:title={'center':'Distortion Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='distortion score'>
In [36]:
sns.set()
In [37]:
# elbow method
n_clusters = 20
cost = []
for i in range(2, n_clusters):
    kmean= KMeans(i, n_init=10, max_iter=1000)
    kmean.fit(X_scaled)
    labels = kmean.labels_
    
    cost.append(kmean.inertia_)
    print("Cluster {} Sillhoute Score {}".format(i, silhouette_score(without_compound, labels)))  

plt.title("Elbow Method")
plt.plot(cost, 'bx-');

Cluster 2 Sillhoute Score 0.5834159976927242
Cluster 3 Sillhoute Score 0.4619443890753173
Cluster 4 Sillhoute Score 0.3260162289955886
Cluster 5 Sillhoute Score 0.3375511220996368
Cluster 6 Sillhoute Score 0.22889785175640734
Cluster 7 Sillhoute Score 0.2179995116659977
Cluster 8 Sillhoute Score 0.19405851201962937
Cluster 9 Sillhoute Score 0.18827055457714917
Cluster 10 Sillhoute Score 0.19000213056913812
Cluster 11 Sillhoute Score 0.08623036110180443
Cluster 12 Sillhoute Score 0.10108716322128757
Cluster 13 Sillhoute Score 0.06277159205146708
Cluster 14 Sillhoute Score 0.01811190398429743
Cluster 15 Sillhoute Score 0.059182994555299
Cluster 16 Sillhoute Score -0.06234871662307226
Cluster 17 Sillhoute Score 0.07262932898934762
Cluster 18 Sillhoute Score -0.014300273482830523
Cluster 19 Sillhoute Score -0.006581185234594802
In [38]:
kmean = KMeans(4, n_init=10, max_iter=1000)
kmean.fit(X_scaled)
labels = kmean.labels_
In [39]:
# adding clusters to our data
clusters = pd.concat([without_compound, pd.DataFrame({'cluster': labels})], axis=1)
clusters.head()
Out[39]:
ALogP ALogp2 AMR apol nAcid naAromAtom nAromBond nAtom ATSc1 ATSc2 ... WTPT-1 WTPT-2 WTPT-3 WTPT-4 WTPT-5 WPATH WPOL XLogP Zagreb cluster
0 -5.0931 25.939668 534.3813 317.363434 6 18 18 293 5.125258 -2.388694 ... 306.516036 1.977523 153.543080 82.176355 71.366725 286334.0 230.0 -8.623 752.0 3
1 0.4325 0.187056 317.0896 187.074612 0 20 21 171 2.269431 -1.093604 ... 174.200943 2.002310 78.030370 30.548034 47.482336 46759.0 130.0 1.134 436.0 3
2 -1.8743 3.513000 325.6625 190.878612 0 20 21 175 2.635543 -1.251529 ... 181.809125 1.997902 88.729976 35.878826 52.851150 52357.0 134.0 -2.012 458.0 3
3 9.1843 84.351366 493.8733 292.709055 0 36 40 266 3.021552 -1.418733 ... 261.668224 1.997467 98.311965 40.550947 57.761019 148212.0 208.0 8.583 660.0 3
4 -2.8933 8.371185 269.4076 154.458752 0 12 12 138 2.148910 -0.961833 ... 147.352827 1.991254 75.875569 30.211599 39.611320 28498.0 110.0 -1.437 360.0 3

5 rows × 223 columns

In [40]:
silhouette_score(without_compound, labels)
Out[40]:
0.32077577396105217

All features¶

In [41]:
# using pca with 2 components to visualize our scaled data with clusters
components = 2

# initilizing and fitting the pca
pca = PCA(n_components=components)
X_pca = pca.fit_transform(X_scaled)

# making dataframe for our pca components
pca_df = pd.DataFrame(X_pca, columns=['PCA1','PCA2'])

# adding cluster assignment
pca_df['cluster'] = pd.Categorical(kmean.labels_)

# adding compound name to this dataframe
# pca_df['compound'] = data['Name']

# get the index of the most important feature on EACH component i.e. largest absolute value
most_important = [np.abs(pca.components_[i]).argmax() for i in range(components)]

# columns/ features of our data
initial_feature_names = without_compound.columns

# get the names
most_important_names = [initial_feature_names[most_important[i]] for i in range(components)]

# using LIST COMPREHENSION HERE AGAIN
dic = {'PC{}'.format(i + 1): most_important_names[i] for i in range(components)}

# explained variance
print("Explained variance ratio", pca.explained_variance_ratio_)
print("\nnSum of Explained variance ratio", pca.explained_variance_ratio_.sum())
print("\nColumns chosen by PCA", dic)


pca_df



# Adjust the figure size
plt.figure(figsize=(12, 8))
# Create the scatterplot using Seaborn
sns.scatterplot(data=pca_df, x='PCA1', y='PCA2', hue="cluster")

# Set the plot title
plt.title("Compounds Clustering - All Features")


# Show the plot
plt.show()
# fig = px.scatter(pca_df, x='PCA1', y='PCA2', color="cluster",  title="Compounds Clustering - All Features", width=800)

# fig.show()
# saving the figure in html file
# fig.write_html("cluster4.html")

Explained variance ratio [0.34087863 0.09107553]

nSum of Explained variance ratio 0.4319541614805529

Columns chosen by PCA {'PC1': 'Zagreb', 'PC2': 'khs.ssssC'}
In [42]:
for c in clusters:
    grid = sns.FacetGrid(clusters, col="cluster")
    grid.map(plt.hist, c)

99% of Components¶

In [43]:
# using pca with 2 components to visualize our scaled data with clusters
components = 0.99

# initilizing and fitting the pca
pca = PCA(n_components=components)
X_pca = pca.fit_transform(X_scaled)
pca.n_components_
Out[43]:
82
In [44]:
kmean = KMeans(4, n_init=10, max_iter=1000)
kmean.fit(X_pca)
labels = kmean.labels_
In [45]:
silhouette_score(without_compound, labels)
Out[45]:
0.3260162289955886
In [46]:
# using pca with 2 components to visualize our scaled data with clusters
components = 2

# initilizing and fitting the pca
pca = PCA(n_components=components)
X_pca = pca.fit_transform(X_pca)

# making dataframe for our pca components
pca_df = pd.DataFrame(X_pca, columns=['PCA1','PCA2'])

# adding cluster assignment
pca_df['cluster'] = pd.Categorical(kmean.labels_)

# adding compound name to this dataframe
# pca_df['compound'] = data['Name']

# get the index of the most important feature on EACH component i.e. largest absolute value
most_important = [np.abs(pca.components_[i]).argmax() for i in range(components)]

# columns/ features of our data
initial_feature_names = without_compound.columns

# get the names
most_important_names = [initial_feature_names[most_important[i]] for i in range(components)]

# using LIST COMPREHENSION HERE AGAIN
dic = {'PC{}'.format(i + 1): most_important_names[i] for i in range(components)}

# explained variance
print("Explained variance ratio", pca.explained_variance_ratio_)
print("\nnSum of Explained variance ratio", pca.explained_variance_ratio_.sum())
print("\nColumns chosen by PCA", dic)


pca_df
# Adjust the figure size
plt.figure(figsize=(12, 8))
# Create the scatterplot using Seaborn
sns.scatterplot(data=pca_df, x='PCA1', y='PCA2', hue="cluster")

# Set the plot title
plt.title("Compounds Clustering - All Features")


# fig = px.scatter(pca_df, x='PCA1', y='PCA2', color="cluster", title="Compounds Clustering - 99% Components PCA", width=800)

# # saving the figure in html file
# # fig.write_html("cluster4.html")

# fig.show()

Explained variance ratio [0.34426944 0.09198149]

nSum of Explained variance ratio 0.43625092682357886

Columns chosen by PCA {'PC1': 'ALogP', 'PC2': 'ALogp2'}
Out[46]:
Text(0.5, 1.0, 'Compounds Clustering - All Features')
In [47]:
# adding clusters to our data
clusters = pd.concat([without_compound, pd.DataFrame({'cluster': labels})], axis=1)
clusters.head(3)
Out[47]:
ALogP ALogp2 AMR apol nAcid naAromAtom nAromBond nAtom ATSc1 ATSc2 ... WTPT-1 WTPT-2 WTPT-3 WTPT-4 WTPT-5 WPATH WPOL XLogP Zagreb cluster
0 -5.0931 25.939668 534.3813 317.363434 6 18 18 293 5.125258 -2.388694 ... 306.516036 1.977523 153.543080 82.176355 71.366725 286334.0 230.0 -8.623 752.0 3
1 0.4325 0.187056 317.0896 187.074612 0 20 21 171 2.269431 -1.093604 ... 174.200943 2.002310 78.030370 30.548034 47.482336 46759.0 130.0 1.134 436.0 3
2 -1.8743 3.513000 325.6625 190.878612 0 20 21 175 2.635543 -1.251529 ... 181.809125 1.997902 88.729976 35.878826 52.851150 52357.0 134.0 -2.012 458.0 3

3 rows × 223 columns

In [48]:
for c in clusters:
    grid = sns.FacetGrid(clusters, col="cluster")
    grid.map(plt.hist, c)

2 Components¶

In [49]:
# using pca with 2 components to visualize our scaled data with clusters
components = 2

# initilizing and fitting the pca
pca = PCA(n_components=components)
X_pca = pca.fit_transform(X_scaled)
pca.n_components_
Out[49]:
2
In [50]:
kmean = KMeans(4, n_init=10, max_iter=1000)
kmean.fit(X_pca)
labels = kmean.labels_
In [51]:
silhouette_score(without_compound, labels)
Out[51]:
0.3392598203636521
In [52]:
# using pca with 2 components to visualize our scaled data with clusters
components = 2

# initilizing and fitting the pca
pca = PCA(n_components=components)
X_pca = pca.fit_transform(X_pca)

# making dataframe for our pca components
pca_df = pd.DataFrame(X_pca, columns=['PCA1','PCA2'])

# adding cluster assignment
pca_df['cluster'] = pd.Categorical(kmean.labels_)

# adding compound name to this dataframe
# pca_df['compound'] = data['Name']

# get the index of the most important feature on EACH component i.e. largest absolute value
most_important = [np.abs(pca.components_[i]).argmax() for i in range(components)]

# columns/ features of our data
initial_feature_names = without_compound.columns

# get the names
most_important_names = [initial_feature_names[most_important[i]] for i in range(components)]

# using LIST COMPREHENSION HERE AGAIN
dic = {'PC{}'.format(i + 1): most_important_names[i] for i in range(components)}

# explained variance
print("Explained variance ratio", pca.explained_variance_ratio_)
print("\nnSum of Explained variance ratio", pca.explained_variance_ratio_.sum())
print("\nColumns chosen by PCA", dic)


pca_df

# Adjust the figure size
plt.figure(figsize=(12, 8))
# Create the scatterplot using Seaborn
sns.scatterplot(data=pca_df, x='PCA1', y='PCA2', hue="cluster")

# Set the plot title
plt.title("Compounds Clustering - All Features")

# fig = px.scatter(pca_df, x='PCA1', y='PCA2', color="cluster", title="Compounds Clustering - 2 Components PCA", width=800)

# # saving the figure in html file
# # fig.write_html("cluster4.html")

# fig.show()

Explained variance ratio [0.78915463 0.21084537]

nSum of Explained variance ratio 0.9999999999999999

Columns chosen by PCA {'PC1': 'ALogP', 'PC2': 'ALogp2'}
Out[52]:
Text(0.5, 1.0, 'Compounds Clustering - All Features')
In [53]:
# adding clusters to our data
clusters = pd.concat([without_compound, pd.DataFrame({'cluster': labels})], axis=1)
clusters.head(3)
Out[53]:
ALogP ALogp2 AMR apol nAcid naAromAtom nAromBond nAtom ATSc1 ATSc2 ... WTPT-1 WTPT-2 WTPT-3 WTPT-4 WTPT-5 WPATH WPOL XLogP Zagreb cluster
0 -5.0931 25.939668 534.3813 317.363434 6 18 18 293 5.125258 -2.388694 ... 306.516036 1.977523 153.543080 82.176355 71.366725 286334.0 230.0 -8.623 752.0 1
1 0.4325 0.187056 317.0896 187.074612 0 20 21 171 2.269431 -1.093604 ... 174.200943 2.002310 78.030370 30.548034 47.482336 46759.0 130.0 1.134 436.0 1
2 -1.8743 3.513000 325.6625 190.878612 0 20 21 175 2.635543 -1.251529 ... 181.809125 1.997902 88.729976 35.878826 52.851150 52357.0 134.0 -2.012 458.0 1

3 rows × 223 columns

In [54]:
for c in clusters:
    grid = sns.FacetGrid(clusters, col="cluster")
    grid.map(plt.hist, c)

Interpretation¶

In [19]:
# for c in clusters:jupyter nbconvert --execute --to html clustering.ipynb

#     grid = sns.FacetGrid(clusters, col="cluster")
#     grid.map(sns.boxplot, c)